Overview

Brought to you by YData

Dataset statistics

Number of variables30
Number of observations1500
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory351.7 KiB
Average record size in memory240.1 B

Variable types

Text1
Numeric14
Categorical13
Boolean1
DateTime1

Alerts

Age is highly overall correlated with Years_ExperienceHigh correlation
Efficiency_Rating is highly overall correlated with Innovation_Score and 3 other fieldsHigh correlation
Innovation_Score is highly overall correlated with Efficiency_Rating and 3 other fieldsHigh correlation
Job_Level is highly overall correlated with Years_ExperienceHigh correlation
Productivity_Score is highly overall correlated with Efficiency_Rating and 3 other fieldsHigh correlation
Quality_Score is highly overall correlated with Efficiency_Rating and 3 other fieldsHigh correlation
Task_Completion_Rate is highly overall correlated with Efficiency_Rating and 3 other fieldsHigh correlation
Years_Experience is highly overall correlated with Age and 1 other fieldsHigh correlation
Employee_ID has unique values Unique
Years_Experience has 259 (17.3%) zeros Zeros
WFH_Days_Per_Week has 137 (9.1%) zeros Zeros
Commute_Time_Minutes has 266 (17.7%) zeros Zeros

Reproduction

Analysis started2025-07-21 00:29:49.381670
Analysis finished2025-07-21 00:30:03.510746
Duration14.13 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

Employee_ID
Text

Unique 

Distinct1500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:03.670702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters10500
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1500 ?
Unique (%)100.0%

Sample

1st rowEMP0001
2nd rowEMP0002
3rd rowEMP0003
4th rowEMP0004
5th rowEMP0005
ValueCountFrequency (%)
emp0013 1
 
0.1%
emp1500 1
 
0.1%
emp0001 1
 
0.1%
emp0002 1
 
0.1%
emp0003 1
 
0.1%
emp0004 1
 
0.1%
emp0005 1
 
0.1%
emp0006 1
 
0.1%
emp0007 1
 
0.1%
emp0008 1
 
0.1%
Other values (1490) 1490
99.3%
2025-07-20T18:30:03.908490image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 1500
14.3%
M 1500
14.3%
P 1500
14.3%
0 1498
14.3%
1 1001
9.5%
3 500
 
4.8%
2 500
 
4.8%
4 500
 
4.8%
5 401
 
3.8%
6 400
 
3.8%
Other values (3) 1200
11.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 1500
14.3%
M 1500
14.3%
P 1500
14.3%
0 1498
14.3%
1 1001
9.5%
3 500
 
4.8%
2 500
 
4.8%
4 500
 
4.8%
5 401
 
3.8%
6 400
 
3.8%
Other values (3) 1200
11.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 1500
14.3%
M 1500
14.3%
P 1500
14.3%
0 1498
14.3%
1 1001
9.5%
3 500
 
4.8%
2 500
 
4.8%
4 500
 
4.8%
5 401
 
3.8%
6 400
 
3.8%
Other values (3) 1200
11.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 1500
14.3%
M 1500
14.3%
P 1500
14.3%
0 1498
14.3%
1 1001
9.5%
3 500
 
4.8%
2 500
 
4.8%
4 500
 
4.8%
5 401
 
3.8%
6 400
 
3.8%
Other values (3) 1200
11.4%

Age
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.190667
Minimum22
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:03.975451image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile22
Q129
median35
Q341
95-th percentile50
Maximum65
Range43
Interquartile range (IQR)12

Descriptive statistics

Standard deviation8.4069077
Coefficient of variation (CV)0.23889595
Kurtosis-0.36226522
Mean35.190667
Median Absolute Deviation (MAD)6
Skewness0.34046357
Sum52786
Variance70.676097
MonotonicityNot monotonic
2025-07-20T18:30:04.046930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
22 125
 
8.3%
35 73
 
4.9%
37 71
 
4.7%
40 69
 
4.6%
36 69
 
4.6%
34 68
 
4.5%
30 66
 
4.4%
29 64
 
4.3%
38 63
 
4.2%
32 61
 
4.1%
Other values (30) 771
51.4%
ValueCountFrequency (%)
22 125
8.3%
23 30
 
2.0%
24 26
 
1.7%
25 37
 
2.5%
26 41
 
2.7%
27 42
 
2.8%
28 52
3.5%
29 64
4.3%
30 66
4.4%
31 45
 
3.0%
ValueCountFrequency (%)
65 1
 
0.1%
62 1
 
0.1%
59 1
 
0.1%
58 6
0.4%
57 5
0.3%
56 2
 
0.1%
55 6
0.4%
54 12
0.8%
53 11
0.7%
52 4
 
0.3%

Years_Experience
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5946667
Minimum0
Maximum32
Zeros259
Zeros (%)17.3%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:04.108146image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q37
95-th percentile14
Maximum32
Range32
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.7406069
Coefficient of variation (CV)1.031763
Kurtosis3.8609257
Mean4.5946667
Median Absolute Deviation (MAD)2
Skewness1.683703
Sum6892
Variance22.473354
MonotonicityNot monotonic
2025-07-20T18:30:04.168483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 259
17.3%
1 227
15.1%
2 153
10.2%
3 139
9.3%
4 123
8.2%
5 110
7.3%
6 95
 
6.3%
7 74
 
4.9%
8 64
 
4.3%
9 56
 
3.7%
Other values (21) 200
13.3%
ValueCountFrequency (%)
0 259
17.3%
1 227
15.1%
2 153
10.2%
3 139
9.3%
4 123
8.2%
5 110
7.3%
6 95
 
6.3%
7 74
 
4.9%
8 64
 
4.3%
9 56
 
3.7%
ValueCountFrequency (%)
32 1
 
0.1%
31 1
 
0.1%
29 1
 
0.1%
28 1
 
0.1%
27 1
 
0.1%
25 1
 
0.1%
24 4
0.3%
23 3
0.2%
22 1
 
0.1%
21 4
0.3%

WFH_Days_Per_Week
Real number (ℝ)

Zeros 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8573333
Minimum0
Maximum5
Zeros137
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:04.227028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q34
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4898969
Coefficient of variation (CV)0.52142915
Kurtosis-0.81815308
Mean2.8573333
Median Absolute Deviation (MAD)1
Skewness-0.38487106
Sum4286
Variance2.2197928
MonotonicityNot monotonic
2025-07-20T18:30:04.273215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
4 405
27.0%
3 330
22.0%
2 264
17.6%
5 196
13.1%
1 168
11.2%
0 137
 
9.1%
ValueCountFrequency (%)
0 137
 
9.1%
1 168
11.2%
2 264
17.6%
3 330
22.0%
4 405
27.0%
5 196
13.1%
ValueCountFrequency (%)
5 196
13.1%
4 405
27.0%
3 330
22.0%
2 264
17.6%
1 168
11.2%
0 137
 
9.1%

Gender
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Female
737 
Male
703 
Non-binary
 
60

Length

Max length10
Median length6
Mean length5.2226667
Min length4

Characters and Unicode

Total characters7834
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFemale
2nd rowFemale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Female 737
49.1%
Male 703
46.9%
Non-binary 60
 
4.0%

Length

2025-07-20T18:30:04.337229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:04.383584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
female 737
49.1%
male 703
46.9%
non-binary 60
 
4.0%

Most occurring characters

ValueCountFrequency (%)
e 2177
27.8%
a 1500
19.1%
l 1440
18.4%
F 737
 
9.4%
m 737
 
9.4%
M 703
 
9.0%
n 120
 
1.5%
N 60
 
0.8%
o 60
 
0.8%
- 60
 
0.8%
Other values (4) 240
 
3.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2177
27.8%
a 1500
19.1%
l 1440
18.4%
F 737
 
9.4%
m 737
 
9.4%
M 703
 
9.0%
n 120
 
1.5%
N 60
 
0.8%
o 60
 
0.8%
- 60
 
0.8%
Other values (4) 240
 
3.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2177
27.8%
a 1500
19.1%
l 1440
18.4%
F 737
 
9.4%
m 737
 
9.4%
M 703
 
9.0%
n 120
 
1.5%
N 60
 
0.8%
o 60
 
0.8%
- 60
 
0.8%
Other values (4) 240
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7834
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2177
27.8%
a 1500
19.1%
l 1440
18.4%
F 737
 
9.4%
m 737
 
9.4%
M 703
 
9.0%
n 120
 
1.5%
N 60
 
0.8%
o 60
 
0.8%
- 60
 
0.8%
Other values (4) 240
 
3.1%

Education_Level
Categorical

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Bachelor Degree
673 
Master Degree
489 
Associate Degree
153 
PhD
88 
High School
72 

Length

Max length19
Median length16
Mean length13.620667
Min length3

Characters and Unicode

Total characters20431
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAssociate Degree
2nd rowMaster Degree
3rd rowPhD
4th rowBachelor Degree
5th rowHigh School

Common Values

ValueCountFrequency (%)
Bachelor Degree 673
44.9%
Master Degree 489
32.6%
Associate Degree 153
 
10.2%
PhD 88
 
5.9%
High School 72
 
4.8%
Professional Degree 25
 
1.7%

Length

2025-07-20T18:30:04.436207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:04.490986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
degree 1340
46.0%
bachelor 673
23.1%
master 489
 
16.8%
associate 153
 
5.3%
phd 88
 
3.0%
high 72
 
2.5%
school 72
 
2.5%
professional 25
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 5360
26.2%
r 2527
12.4%
D 1428
 
7.0%
1412
 
6.9%
g 1412
 
6.9%
a 1340
 
6.6%
o 1020
 
5.0%
h 905
 
4.4%
c 898
 
4.4%
s 845
 
4.1%
Other values (11) 3284
16.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 5360
26.2%
r 2527
12.4%
D 1428
 
7.0%
1412
 
6.9%
g 1412
 
6.9%
a 1340
 
6.6%
o 1020
 
5.0%
h 905
 
4.4%
c 898
 
4.4%
s 845
 
4.1%
Other values (11) 3284
16.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 5360
26.2%
r 2527
12.4%
D 1428
 
7.0%
1412
 
6.9%
g 1412
 
6.9%
a 1340
 
6.6%
o 1020
 
5.0%
h 905
 
4.4%
c 898
 
4.4%
s 845
 
4.1%
Other values (11) 3284
16.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 5360
26.2%
r 2527
12.4%
D 1428
 
7.0%
1412
 
6.9%
g 1412
 
6.9%
a 1340
 
6.6%
o 1020
 
5.0%
h 905
 
4.4%
c 898
 
4.4%
s 845
 
4.1%
Other values (11) 3284
16.1%

Marital_Status
Categorical

Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Married
775 
Single
417 
Divorced
186 
In Relationship
122 

Length

Max length15
Median length7
Mean length7.4966667
Min length6

Characters and Unicode

Total characters11245
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMarried
2nd rowMarried
3rd rowSingle
4th rowMarried
5th rowDivorced

Common Values

ValueCountFrequency (%)
Married 775
51.7%
Single 417
27.8%
Divorced 186
 
12.4%
In Relationship 122
 
8.1%

Length

2025-07-20T18:30:04.559266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:04.603003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
married 775
47.8%
single 417
25.7%
divorced 186
 
11.5%
in 122
 
7.5%
relationship 122
 
7.5%

Most occurring characters

ValueCountFrequency (%)
r 1736
15.4%
i 1622
14.4%
e 1500
13.3%
d 961
8.5%
a 897
8.0%
M 775
6.9%
n 661
 
5.9%
l 539
 
4.8%
S 417
 
3.7%
g 417
 
3.7%
Other values (11) 1720
15.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11245
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 1736
15.4%
i 1622
14.4%
e 1500
13.3%
d 961
8.5%
a 897
8.0%
M 775
6.9%
n 661
 
5.9%
l 539
 
4.8%
S 417
 
3.7%
g 417
 
3.7%
Other values (11) 1720
15.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11245
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 1736
15.4%
i 1622
14.4%
e 1500
13.3%
d 961
8.5%
a 897
8.0%
M 775
6.9%
n 661
 
5.9%
l 539
 
4.8%
S 417
 
3.7%
g 417
 
3.7%
Other values (11) 1720
15.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11245
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 1736
15.4%
i 1622
14.4%
e 1500
13.3%
d 961
8.5%
a 897
8.0%
M 775
6.9%
n 661
 
5.9%
l 539
 
4.8%
S 417
 
3.7%
g 417
 
3.7%
Other values (11) 1720
15.3%
Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
False
824 
True
676 
ValueCountFrequency (%)
False 824
54.9%
True 676
45.1%
2025-07-20T18:30:04.642375image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Location_Type
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Suburban
672 
Urban
664 
Rural
164 

Length

Max length8
Median length5
Mean length6.344
Min length5

Characters and Unicode

Total characters9516
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUrban
2nd rowUrban
3rd rowRural
4th rowUrban
5th rowRural

Common Values

ValueCountFrequency (%)
Suburban 672
44.8%
Urban 664
44.3%
Rural 164
 
10.9%

Length

2025-07-20T18:30:04.687739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:04.727790image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
suburban 672
44.8%
urban 664
44.3%
rural 164
 
10.9%

Most occurring characters

ValueCountFrequency (%)
b 2008
21.1%
u 1508
15.8%
r 1500
15.8%
a 1500
15.8%
n 1336
14.0%
S 672
 
7.1%
U 664
 
7.0%
R 164
 
1.7%
l 164
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9516
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 2008
21.1%
u 1508
15.8%
r 1500
15.8%
a 1500
15.8%
n 1336
14.0%
S 672
 
7.1%
U 664
 
7.0%
R 164
 
1.7%
l 164
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9516
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 2008
21.1%
u 1508
15.8%
r 1500
15.8%
a 1500
15.8%
n 1336
14.0%
S 672
 
7.1%
U 664
 
7.0%
R 164
 
1.7%
l 164
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9516
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 2008
21.1%
u 1508
15.8%
r 1500
15.8%
a 1500
15.8%
n 1336
14.0%
S 672
 
7.1%
U 664
 
7.0%
R 164
 
1.7%
l 164
 
1.7%

Department
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Engineering
455 
Sales
202 
Marketing
176 
Operations
137 
Customer Success
120 
Other values (5)
410 

Length

Max length16
Median length11
Mean length8.9826667
Min length2

Characters and Unicode

Total characters13474
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowProduct
2nd rowCustomer Success
3rd rowOperations
4th rowFinance
5th rowEngineering

Common Values

ValueCountFrequency (%)
Engineering 455
30.3%
Sales 202
13.5%
Marketing 176
 
11.7%
Operations 137
 
9.1%
Customer Success 120
 
8.0%
Finance 112
 
7.5%
HR 101
 
6.7%
Product 81
 
5.4%
Design 60
 
4.0%
Data Science 56
 
3.7%

Length

2025-07-20T18:30:04.982679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.041887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
engineering 455
27.1%
sales 202
12.1%
marketing 176
 
10.5%
operations 137
 
8.2%
customer 120
 
7.2%
success 120
 
7.2%
finance 112
 
6.7%
hr 101
 
6.0%
product 81
 
4.8%
design 60
 
3.6%
Other values (2) 112
 
6.7%

Most occurring characters

ValueCountFrequency (%)
n 2018
15.0%
e 1949
14.5%
i 1451
10.8%
g 1146
 
8.5%
r 969
 
7.2%
s 759
 
5.6%
a 739
 
5.5%
t 570
 
4.2%
c 545
 
4.0%
E 455
 
3.4%
Other values (17) 2873
21.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13474
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 2018
15.0%
e 1949
14.5%
i 1451
10.8%
g 1146
 
8.5%
r 969
 
7.2%
s 759
 
5.6%
a 739
 
5.5%
t 570
 
4.2%
c 545
 
4.0%
E 455
 
3.4%
Other values (17) 2873
21.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13474
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 2018
15.0%
e 1949
14.5%
i 1451
10.8%
g 1146
 
8.5%
r 969
 
7.2%
s 759
 
5.6%
a 739
 
5.5%
t 570
 
4.2%
c 545
 
4.0%
E 455
 
3.4%
Other values (17) 2873
21.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13474
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 2018
15.0%
e 1949
14.5%
i 1451
10.8%
g 1146
 
8.5%
r 969
 
7.2%
s 759
 
5.6%
a 739
 
5.5%
t 570
 
4.2%
c 545
 
4.0%
E 455
 
3.4%
Other values (17) 2873
21.3%

Job_Level
Categorical

High correlation 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Junior
539 
Mid-Level
422 
Senior
322 
Lead
149 
Manager
61 

Length

Max length9
Median length6
Mean length6.6953333
Min length4

Characters and Unicode

Total characters10043
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMid-Level
2nd rowSenior
3rd rowMid-Level
4th rowManager
5th rowSenior

Common Values

ValueCountFrequency (%)
Junior 539
35.9%
Mid-Level 422
28.1%
Senior 322
21.5%
Lead 149
 
9.9%
Manager 61
 
4.1%
Director 7
 
0.5%

Length

2025-07-20T18:30:05.123961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.169791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
junior 539
35.9%
mid-level 422
28.1%
senior 322
21.5%
lead 149
 
9.9%
manager 61
 
4.1%
director 7
 
0.5%

Most occurring characters

ValueCountFrequency (%)
e 1383
13.8%
i 1290
12.8%
r 936
9.3%
n 922
9.2%
o 868
8.6%
L 571
 
5.7%
d 571
 
5.7%
u 539
 
5.4%
J 539
 
5.4%
M 483
 
4.8%
Other values (9) 1941
19.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 10043
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1383
13.8%
i 1290
12.8%
r 936
9.3%
n 922
9.2%
o 868
8.6%
L 571
 
5.7%
d 571
 
5.7%
u 539
 
5.4%
J 539
 
5.4%
M 483
 
4.8%
Other values (9) 1941
19.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 10043
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1383
13.8%
i 1290
12.8%
r 936
9.3%
n 922
9.2%
o 868
8.6%
L 571
 
5.7%
d 571
 
5.7%
u 539
 
5.4%
J 539
 
5.4%
M 483
 
4.8%
Other values (9) 1941
19.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 10043
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1383
13.8%
i 1290
12.8%
r 936
9.3%
n 922
9.2%
o 868
8.6%
L 571
 
5.7%
d 571
 
5.7%
u 539
 
5.4%
J 539
 
5.4%
M 483
 
4.8%
Other values (9) 1941
19.3%

Company_Size
Categorical

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Large (1001-5000)
386 
Medium (201-1000)
384 
Small (51-200)
352 
Startup (1-50)
225 
Enterprise (5000+)
153 

Length

Max length18
Median length17
Mean length15.948
Min length14

Characters and Unicode

Total characters23922
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLarge (1001-5000)
2nd rowStartup (1-50)
3rd rowMedium (201-1000)
4th rowMedium (201-1000)
5th rowSmall (51-200)

Common Values

ValueCountFrequency (%)
Large (1001-5000) 386
25.7%
Medium (201-1000) 384
25.6%
Small (51-200) 352
23.5%
Startup (1-50) 225
15.0%
Enterprise (5000+) 153
 
10.2%

Length

2025-07-20T18:30:05.235110image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.281233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
large 386
12.9%
1001-5000 386
12.9%
medium 384
12.8%
201-1000 384
12.8%
small 352
11.7%
51-200 352
11.7%
startup 225
7.5%
1-50 225
7.5%
enterprise 153
 
5.1%
5000 153
 
5.1%

Most occurring characters

ValueCountFrequency (%)
0 4854
20.3%
1 2117
 
8.8%
1500
 
6.3%
( 1500
 
6.3%
) 1500
 
6.3%
- 1347
 
5.6%
5 1116
 
4.7%
e 1076
 
4.5%
a 963
 
4.0%
r 917
 
3.8%
Other values (16) 7032
29.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23922
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4854
20.3%
1 2117
 
8.8%
1500
 
6.3%
( 1500
 
6.3%
) 1500
 
6.3%
- 1347
 
5.6%
5 1116
 
4.7%
e 1076
 
4.5%
a 963
 
4.0%
r 917
 
3.8%
Other values (16) 7032
29.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23922
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4854
20.3%
1 2117
 
8.8%
1500
 
6.3%
( 1500
 
6.3%
) 1500
 
6.3%
- 1347
 
5.6%
5 1116
 
4.7%
e 1076
 
4.5%
a 963
 
4.0%
r 917
 
3.8%
Other values (16) 7032
29.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23922
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4854
20.3%
1 2117
 
8.8%
1500
 
6.3%
( 1500
 
6.3%
) 1500
 
6.3%
- 1347
 
5.6%
5 1116
 
4.7%
e 1076
 
4.5%
a 963
 
4.0%
r 917
 
3.8%
Other values (16) 7032
29.4%

Industry
Categorical

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Technology
475 
Finance
223 
Healthcare
185 
Education
144 
Retail
106 
Other values (5)
367 

Length

Max length13
Median length10
Mean length9.1213333
Min length5

Characters and Unicode

Total characters13682
Distinct characters29
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinance
2nd rowEducation
3rd rowTechnology
4th rowTechnology
5th rowTechnology

Common Values

ValueCountFrequency (%)
Technology 475
31.7%
Finance 223
14.9%
Healthcare 185
 
12.3%
Education 144
 
9.6%
Retail 106
 
7.1%
Manufacturing 93
 
6.2%
Consulting 88
 
5.9%
Government 74
 
4.9%
Media 72
 
4.8%
Non-profit 40
 
2.7%

Length

2025-07-20T18:30:05.348620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.404984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
technology 475
31.7%
finance 223
14.9%
healthcare 185
 
12.3%
education 144
 
9.6%
retail 106
 
7.1%
manufacturing 93
 
6.2%
consulting 88
 
5.9%
government 74
 
4.9%
media 72
 
4.8%
non-profit 40
 
2.7%

Most occurring characters

ValueCountFrequency (%)
n 1615
11.8%
e 1394
10.2%
o 1336
 
9.8%
c 1120
 
8.2%
a 1101
 
8.0%
l 854
 
6.2%
i 766
 
5.6%
t 730
 
5.3%
h 660
 
4.8%
g 656
 
4.8%
Other values (19) 3450
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13682
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 1615
11.8%
e 1394
10.2%
o 1336
 
9.8%
c 1120
 
8.2%
a 1101
 
8.0%
l 854
 
6.2%
i 766
 
5.6%
t 730
 
5.3%
h 660
 
4.8%
g 656
 
4.8%
Other values (19) 3450
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13682
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 1615
11.8%
e 1394
10.2%
o 1336
 
9.8%
c 1120
 
8.2%
a 1101
 
8.0%
l 854
 
6.2%
i 766
 
5.6%
t 730
 
5.3%
h 660
 
4.8%
g 656
 
4.8%
Other values (19) 3450
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13682
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 1615
11.8%
e 1394
10.2%
o 1336
 
9.8%
c 1120
 
8.2%
a 1101
 
8.0%
l 854
 
6.2%
i 766
 
5.6%
t 730
 
5.3%
h 660
 
4.8%
g 656
 
4.8%
Other values (19) 3450
25.2%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Good
595 
Average
452 
Excellent
340 
Poor
113 

Length

Max length9
Median length7
Mean length6.0373333
Min length4

Characters and Unicode

Total characters9056
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGood
2nd rowGood
3rd rowExcellent
4th rowExcellent
5th rowAverage

Common Values

ValueCountFrequency (%)
Good 595
39.7%
Average 452
30.1%
Excellent 340
22.7%
Poor 113
 
7.5%

Length

2025-07-20T18:30:05.483914image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.526575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
good 595
39.7%
average 452
30.1%
excellent 340
22.7%
poor 113
 
7.5%

Most occurring characters

ValueCountFrequency (%)
e 1584
17.5%
o 1416
15.6%
l 680
 
7.5%
d 595
 
6.6%
G 595
 
6.6%
r 565
 
6.2%
v 452
 
5.0%
A 452
 
5.0%
a 452
 
5.0%
g 452
 
5.0%
Other values (6) 1813
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1584
17.5%
o 1416
15.6%
l 680
 
7.5%
d 595
 
6.6%
G 595
 
6.6%
r 565
 
6.2%
v 452
 
5.0%
A 452
 
5.0%
a 452
 
5.0%
g 452
 
5.0%
Other values (6) 1813
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1584
17.5%
o 1416
15.6%
l 680
 
7.5%
d 595
 
6.6%
G 595
 
6.6%
r 565
 
6.2%
v 452
 
5.0%
A 452
 
5.0%
a 452
 
5.0%
g 452
 
5.0%
Other values (6) 1813
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1584
17.5%
o 1416
15.6%
l 680
 
7.5%
d 595
 
6.6%
G 595
 
6.6%
r 565
 
6.2%
v 452
 
5.0%
A 452
 
5.0%
a 452
 
5.0%
g 452
 
5.0%
Other values (6) 1813
20.0%
Distinct4
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Very Fast (100+ Mbps)
691 
Fast (50-100 Mbps)
505 
Moderate (25-50 Mbps)
231 
Slow (<25 Mbps)
73 

Length

Max length21
Median length21
Mean length19.698
Min length15

Characters and Unicode

Total characters29547
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery Fast (100+ Mbps)
2nd rowVery Fast (100+ Mbps)
3rd rowFast (50-100 Mbps)
4th rowVery Fast (100+ Mbps)
5th rowModerate (25-50 Mbps)

Common Values

ValueCountFrequency (%)
Very Fast (100+ Mbps) 691
46.1%
Fast (50-100 Mbps) 505
33.7%
Moderate (25-50 Mbps) 231
 
15.4%
Slow (<25 Mbps) 73
 
4.9%

Length

2025-07-20T18:30:05.579372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.622121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mbps 1500
28.9%
fast 1196
23.0%
very 691
13.3%
100 691
13.3%
50-100 505
 
9.7%
moderate 231
 
4.5%
25-50 231
 
4.5%
slow 73
 
1.4%
25 73
 
1.4%

Most occurring characters

ValueCountFrequency (%)
3691
 
12.5%
0 3128
 
10.6%
s 2696
 
9.1%
M 1731
 
5.9%
( 1500
 
5.1%
p 1500
 
5.1%
b 1500
 
5.1%
) 1500
 
5.1%
t 1427
 
4.8%
a 1427
 
4.8%
Other values (16) 9447
32.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29547
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3691
 
12.5%
0 3128
 
10.6%
s 2696
 
9.1%
M 1731
 
5.9%
( 1500
 
5.1%
p 1500
 
5.1%
b 1500
 
5.1%
) 1500
 
5.1%
t 1427
 
4.8%
a 1427
 
4.8%
Other values (16) 9447
32.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29547
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3691
 
12.5%
0 3128
 
10.6%
s 2696
 
9.1%
M 1731
 
5.9%
( 1500
 
5.1%
p 1500
 
5.1%
b 1500
 
5.1%
) 1500
 
5.1%
t 1427
 
4.8%
a 1427
 
4.8%
Other values (16) 9447
32.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29547
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3691
 
12.5%
0 3128
 
10.6%
s 2696
 
9.1%
M 1731
 
5.9%
( 1500
 
5.1%
p 1500
 
5.1%
b 1500
 
5.1%
) 1500
 
5.1%
t 1427
 
4.8%
a 1427
 
4.8%
Other values (16) 9447
32.0%

Work_Hours_Per_Week
Real number (ℝ)

Distinct37
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.734
Minimum25
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:05.677930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile32
Q137
median42
Q346
95-th percentile51
Maximum65
Range40
Interquartile range (IQR)9

Descriptive statistics

Standard deviation6.0382849
Coefficient of variation (CV)0.14468503
Kurtosis-0.084306208
Mean41.734
Median Absolute Deviation (MAD)4
Skewness-0.095260728
Sum62601
Variance36.460885
MonotonicityNot monotonic
2025-07-20T18:30:05.745203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
45 102
 
6.8%
43 100
 
6.7%
42 96
 
6.4%
39 96
 
6.4%
44 86
 
5.7%
41 84
 
5.6%
46 79
 
5.3%
40 75
 
5.0%
47 74
 
4.9%
36 72
 
4.8%
Other values (27) 636
42.4%
ValueCountFrequency (%)
25 8
 
0.5%
26 4
 
0.3%
27 9
 
0.6%
28 5
 
0.3%
29 5
 
0.3%
30 24
1.6%
31 11
 
0.7%
32 25
1.7%
33 43
2.9%
34 46
3.1%
ValueCountFrequency (%)
65 1
 
0.1%
63 1
 
0.1%
59 2
 
0.1%
58 1
 
0.1%
57 2
 
0.1%
56 4
 
0.3%
55 7
 
0.5%
54 6
 
0.4%
53 19
1.3%
52 21
1.4%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Moderate
542 
High
492 
Very High
253 
Low
174 
Very Low
 
39

Length

Max length9
Median length8
Mean length6.2766667
Min length3

Characters and Unicode

Total characters9415
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLow
2nd rowModerate
3rd rowModerate
4th rowHigh
5th rowVery Low

Common Values

ValueCountFrequency (%)
Moderate 542
36.1%
High 492
32.8%
Very High 253
16.9%
Low 174
 
11.6%
Very Low 39
 
2.6%

Length

2025-07-20T18:30:05.810352image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.855391image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 745
41.6%
moderate 542
30.2%
very 292
 
16.3%
low 213
 
11.9%

Most occurring characters

ValueCountFrequency (%)
e 1376
14.6%
r 834
8.9%
o 755
8.0%
H 745
7.9%
g 745
7.9%
h 745
7.9%
i 745
7.9%
d 542
 
5.8%
t 542
 
5.8%
a 542
 
5.8%
Other values (6) 1844
19.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9415
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1376
14.6%
r 834
8.9%
o 755
8.0%
H 745
7.9%
g 745
7.9%
h 745
7.9%
i 745
7.9%
d 542
 
5.8%
t 542
 
5.8%
a 542
 
5.8%
Other values (6) 1844
19.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9415
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1376
14.6%
r 834
8.9%
o 755
8.0%
H 745
7.9%
g 745
7.9%
h 745
7.9%
i 745
7.9%
d 542
 
5.8%
t 542
 
5.8%
a 542
 
5.8%
Other values (6) 1844
19.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9415
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1376
14.6%
r 834
8.9%
o 755
8.0%
H 745
7.9%
g 745
7.9%
h 745
7.9%
i 745
7.9%
d 542
 
5.8%
t 542
 
5.8%
a 542
 
5.8%
Other values (6) 1844
19.6%
Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Daily
510 
Few times per week
445 
Weekly
340 
Bi-weekly
131 
Monthly
74 

Length

Max length18
Median length9
Mean length9.5313333
Min length5

Characters and Unicode

Total characters14297
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFew times per week
2nd rowMonthly
3rd rowFew times per week
4th rowDaily
5th rowFew times per week

Common Values

ValueCountFrequency (%)
Daily 510
34.0%
Few times per week 445
29.7%
Weekly 340
22.7%
Bi-weekly 131
 
8.7%
Monthly 74
 
4.9%

Length

2025-07-20T18:30:05.915512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:05.960153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
daily 510
18.0%
few 445
15.7%
times 445
15.7%
per 445
15.7%
week 445
15.7%
weekly 340
12.0%
bi-weekly 131
 
4.6%
monthly 74
 
2.6%

Most occurring characters

ValueCountFrequency (%)
e 3167
22.2%
1335
9.3%
i 1086
 
7.6%
l 1055
 
7.4%
y 1055
 
7.4%
w 1021
 
7.1%
k 916
 
6.4%
t 519
 
3.6%
D 510
 
3.6%
a 510
 
3.6%
Other values (12) 3123
21.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14297
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3167
22.2%
1335
9.3%
i 1086
 
7.6%
l 1055
 
7.4%
y 1055
 
7.4%
w 1021
 
7.1%
k 916
 
6.4%
t 519
 
3.6%
D 510
 
3.6%
a 510
 
3.6%
Other values (12) 3123
21.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14297
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3167
22.2%
1335
9.3%
i 1086
 
7.6%
l 1055
 
7.4%
y 1055
 
7.4%
w 1021
 
7.1%
k 916
 
6.4%
t 519
 
3.6%
D 510
 
3.6%
a 510
 
3.6%
Other values (12) 3123
21.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14297
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3167
22.2%
1335
9.3%
i 1086
 
7.6%
l 1055
 
7.4%
y 1055
 
7.4%
w 1021
 
7.1%
k 916
 
6.4%
t 519
 
3.6%
D 510
 
3.6%
a 510
 
3.6%
Other values (12) 3123
21.8%

Productivity_Score
Real number (ℝ)

High correlation 

Distinct438
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.094867
Minimum35
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.022043image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile53.395
Q173.7
median86.7
Q398
95-th percentile98
Maximum98
Range63
Interquartile range (IQR)24.3

Descriptive statistics

Standard deviation15.227276
Coefficient of variation (CV)0.1832517
Kurtosis0.18255892
Mean83.094867
Median Absolute Deviation (MAD)11.3
Skewness-0.94133989
Sum124642.3
Variance231.86992
MonotonicityNot monotonic
2025-07-20T18:30:06.093124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 420
28.0%
35 13
 
0.9%
74.7 9
 
0.6%
95.8 8
 
0.5%
84.2 8
 
0.5%
77.2 7
 
0.5%
94.8 7
 
0.5%
93.2 7
 
0.5%
88.6 7
 
0.5%
84.1 7
 
0.5%
Other values (428) 1007
67.1%
ValueCountFrequency (%)
35 13
0.9%
37.5 1
 
0.1%
38.3 1
 
0.1%
38.7 1
 
0.1%
40.3 2
 
0.1%
40.7 1
 
0.1%
41.2 1
 
0.1%
41.4 1
 
0.1%
41.5 2
 
0.1%
41.8 1
 
0.1%
ValueCountFrequency (%)
98 420
28.0%
97.9 1
 
0.1%
97.8 4
 
0.3%
97.6 2
 
0.1%
97.5 1
 
0.1%
97.3 3
 
0.2%
97.2 5
 
0.3%
97.1 3
 
0.2%
97 1
 
0.1%
96.9 4
 
0.3%

Task_Completion_Rate
Real number (ℝ)

High correlation 

Distinct469
Distinct (%)31.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82.507133
Minimum40
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.162748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile53
Q172.2
median86
Q396.1
95-th percentile100
Maximum100
Range60
Interquartile range (IQR)23.9

Descriptive statistics

Standard deviation15.390392
Coefficient of variation (CV)0.18653408
Kurtosis-0.25775808
Mean82.507133
Median Absolute Deviation (MAD)11.6
Skewness-0.75299066
Sum123760.7
Variance236.86417
MonotonicityNot monotonic
2025-07-20T18:30:06.237301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 234
 
15.6%
40 17
 
1.1%
88 8
 
0.5%
98.3 8
 
0.5%
88.1 8
 
0.5%
91.9 8
 
0.5%
96.5 8
 
0.5%
81.7 7
 
0.5%
94.3 7
 
0.5%
91.6 7
 
0.5%
Other values (459) 1188
79.2%
ValueCountFrequency (%)
40 17
1.1%
40.2 1
 
0.1%
40.3 1
 
0.1%
40.5 1
 
0.1%
41 1
 
0.1%
41.2 1
 
0.1%
41.9 1
 
0.1%
42.1 1
 
0.1%
42.2 1
 
0.1%
43 1
 
0.1%
ValueCountFrequency (%)
100 234
15.6%
99.9 2
 
0.1%
99.8 4
 
0.3%
99.7 6
 
0.4%
99.6 1
 
0.1%
99.5 1
 
0.1%
99.4 4
 
0.3%
99.3 3
 
0.2%
99.2 1
 
0.1%
99.1 6
 
0.4%

Quality_Score
Real number (ℝ)

High correlation 

Distinct422
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.410933
Minimum50
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.312255image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile56.4
Q175.2
median87.6
Q396.225
95-th percentile100
Maximum100
Range50
Interquartile range (IQR)21.025

Descriptive statistics

Standard deviation13.881407
Coefficient of variation (CV)0.16445034
Kurtosis-0.34491642
Mean84.410933
Median Absolute Deviation (MAD)9.95
Skewness-0.78038441
Sum126616.4
Variance192.69345
MonotonicityNot monotonic
2025-07-20T18:30:06.387844image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 215
 
14.3%
50 34
 
2.3%
96.7 11
 
0.7%
93.8 10
 
0.7%
94 10
 
0.7%
90.9 9
 
0.6%
86.6 9
 
0.6%
88.8 9
 
0.6%
93.6 9
 
0.6%
91.6 9
 
0.6%
Other values (412) 1175
78.3%
ValueCountFrequency (%)
50 34
2.3%
50.7 1
 
0.1%
51.1 2
 
0.1%
51.2 2
 
0.1%
51.3 1
 
0.1%
51.6 1
 
0.1%
51.8 1
 
0.1%
51.9 1
 
0.1%
52.1 1
 
0.1%
52.3 3
 
0.2%
ValueCountFrequency (%)
100 215
14.3%
99.9 5
 
0.3%
99.8 3
 
0.2%
99.7 1
 
0.1%
99.6 2
 
0.1%
99.5 3
 
0.2%
99.4 1
 
0.1%
99.3 5
 
0.3%
99.2 6
 
0.4%
99.1 5
 
0.3%

Innovation_Score
Real number (ℝ)

High correlation 

Distinct498
Distinct (%)33.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.821667
Minimum30
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.456970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile45.695
Q162.6
median74.8
Q384.2
95-th percentile95
Maximum95
Range65
Interquartile range (IQR)21.6

Descriptive statistics

Standard deviation14.994149
Coefficient of variation (CV)0.20590231
Kurtosis-0.38720196
Mean72.821667
Median Absolute Deviation (MAD)10.9
Skewness-0.49055104
Sum109232.5
Variance224.82451
MonotonicityNot monotonic
2025-07-20T18:30:06.529796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 101
 
6.7%
83.1 11
 
0.7%
83.6 11
 
0.7%
79.2 10
 
0.7%
68 9
 
0.6%
77.5 9
 
0.6%
62.6 9
 
0.6%
81 8
 
0.5%
73.8 8
 
0.5%
74 8
 
0.5%
Other values (488) 1316
87.7%
ValueCountFrequency (%)
30 5
0.3%
30.8 1
 
0.1%
30.9 1
 
0.1%
32.4 1
 
0.1%
33 1
 
0.1%
33.1 1
 
0.1%
33.3 2
 
0.1%
33.4 1
 
0.1%
33.5 1
 
0.1%
33.9 1
 
0.1%
ValueCountFrequency (%)
95 101
6.7%
94.9 2
 
0.1%
94.8 1
 
0.1%
94.7 1
 
0.1%
94.6 1
 
0.1%
94.5 3
 
0.2%
94.4 1
 
0.1%
94.3 2
 
0.1%
94.2 4
 
0.3%
94.1 1
 
0.1%

Efficiency_Rating
Real number (ℝ)

High correlation 

Distinct268
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean90.488533
Minimum43.4
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.602104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum43.4
5-th percentile69.585
Q190.475
median95
Q395
95-th percentile95
Maximum95
Range51.6
Interquartile range (IQR)4.525

Descriptive statistics

Standard deviation9.0517194
Coefficient of variation (CV)0.10003167
Kurtosis5.5146211
Mean90.488533
Median Absolute Deviation (MAD)0
Skewness-2.367328
Sum135732.8
Variance81.933624
MonotonicityNot monotonic
2025-07-20T18:30:06.671191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95 1006
67.1%
85.9 8
 
0.5%
93.9 6
 
0.4%
84.2 6
 
0.4%
88.2 6
 
0.4%
83.7 5
 
0.3%
89.2 5
 
0.3%
94.2 5
 
0.3%
94.9 5
 
0.3%
91.3 5
 
0.3%
Other values (258) 443
29.5%
ValueCountFrequency (%)
43.4 1
0.1%
44 1
0.1%
45.8 1
0.1%
45.9 1
0.1%
46 1
0.1%
47.4 1
0.1%
49.6 1
0.1%
51 1
0.1%
52.2 2
0.1%
52.3 1
0.1%
ValueCountFrequency (%)
95 1006
67.1%
94.9 5
 
0.3%
94.8 3
 
0.2%
94.7 3
 
0.2%
94.6 2
 
0.1%
94.5 3
 
0.2%
94.4 4
 
0.3%
94.3 3
 
0.2%
94.2 5
 
0.3%
94.1 2
 
0.1%

Meetings_Per_Week
Real number (ℝ)

Distinct19
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.08
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.724739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q16
median8
Q310
95-th percentile13
Maximum19
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9449474
Coefficient of variation (CV)0.36447369
Kurtosis0.0065264837
Mean8.08
Median Absolute Deviation (MAD)2
Skewness0.24384127
Sum12120
Variance8.6727151
MonotonicityNot monotonic
2025-07-20T18:30:06.777814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
8 211
14.1%
7 191
12.7%
9 174
11.6%
10 163
10.9%
6 160
10.7%
5 139
9.3%
11 111
7.4%
12 93
6.2%
4 73
 
4.9%
3 54
 
3.6%
Other values (9) 131
8.7%
ValueCountFrequency (%)
1 6
 
0.4%
2 26
 
1.7%
3 54
 
3.6%
4 73
 
4.9%
5 139
9.3%
6 160
10.7%
7 191
12.7%
8 211
14.1%
9 174
11.6%
10 163
10.9%
ValueCountFrequency (%)
19 2
 
0.1%
18 2
 
0.1%
17 3
 
0.2%
16 5
 
0.3%
15 19
 
1.3%
14 21
 
1.4%
13 47
 
3.1%
12 93
6.2%
11 111
7.4%
10 163
10.9%

Commute_Time_Minutes
Real number (ℝ)

Zeros 

Distinct87
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.465333
Minimum0
Maximum104
Zeros266
Zeros (%)17.7%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.843559image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median25
Q338
95-th percentile58
Maximum104
Range104
Interquartile range (IQR)28

Descriptive statistics

Standard deviation19.056799
Coefficient of variation (CV)0.74834279
Kurtosis0.17287199
Mean25.465333
Median Absolute Deviation (MAD)14
Skewness0.53566861
Sum38198
Variance363.16157
MonotonicityNot monotonic
2025-07-20T18:30:06.918242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 266
 
17.7%
23 37
 
2.5%
31 35
 
2.3%
30 33
 
2.2%
21 33
 
2.2%
33 33
 
2.2%
34 33
 
2.2%
32 32
 
2.1%
25 32
 
2.1%
26 32
 
2.1%
Other values (77) 934
62.3%
ValueCountFrequency (%)
0 266
17.7%
1 7
 
0.5%
2 5
 
0.3%
3 6
 
0.4%
4 5
 
0.3%
5 14
 
0.9%
6 14
 
0.9%
7 17
 
1.1%
8 16
 
1.1%
9 14
 
0.9%
ValueCountFrequency (%)
104 1
 
0.1%
101 1
 
0.1%
99 1
 
0.1%
97 1
 
0.1%
94 1
 
0.1%
90 1
 
0.1%
88 1
 
0.1%
81 1
 
0.1%
80 2
0.1%
79 4
0.3%

Job_Satisfaction
Real number (ℝ)

Distinct261
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean95.3644
Minimum35.2
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:06.987425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.2
5-th percentile72.675
Q197.25
median100
Q3100
95-th percentile100
Maximum100
Range64.8
Interquartile range (IQR)2.75

Descriptive statistics

Standard deviation9.8532264
Coefficient of variation (CV)0.10332185
Kurtosis6.3653917
Mean95.3644
Median Absolute Deviation (MAD)0
Skewness-2.4954133
Sum143046.6
Variance97.08607
MonotonicityNot monotonic
2025-07-20T18:30:07.067422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1081
72.1%
78.4 5
 
0.3%
82 4
 
0.3%
95.7 4
 
0.3%
89.1 4
 
0.3%
87 4
 
0.3%
99.9 4
 
0.3%
91.5 4
 
0.3%
98.5 4
 
0.3%
90.8 3
 
0.2%
Other values (251) 383
 
25.5%
ValueCountFrequency (%)
35.2 1
0.1%
37.6 1
0.1%
48.4 1
0.1%
49.1 1
0.1%
50 1
0.1%
50.3 1
0.1%
50.8 2
0.1%
52.5 1
0.1%
52.6 2
0.1%
53.9 1
0.1%
ValueCountFrequency (%)
100 1081
72.1%
99.9 4
 
0.3%
99.8 1
 
0.1%
99.7 2
 
0.1%
99.6 2
 
0.1%
99.5 1
 
0.1%
99.4 1
 
0.1%
99.3 2
 
0.1%
99.2 2
 
0.1%
99.1 2
 
0.1%

Stress_Level
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6786667
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:07.132133image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q37
95-th percentile9
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0525318
Coefficient of variation (CV)0.36144608
Kurtosis-0.44776743
Mean5.6786667
Median Absolute Deviation (MAD)1
Skewness-0.12811872
Sum8518
Variance4.2128868
MonotonicityNot monotonic
2025-07-20T18:30:07.178941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 317
21.1%
5 254
16.9%
7 211
14.1%
4 186
12.4%
8 179
11.9%
3 114
 
7.6%
9 86
 
5.7%
2 82
 
5.5%
10 38
 
2.5%
1 33
 
2.2%
ValueCountFrequency (%)
1 33
 
2.2%
2 82
 
5.5%
3 114
 
7.6%
4 186
12.4%
5 254
16.9%
6 317
21.1%
7 211
14.1%
8 179
11.9%
9 86
 
5.7%
10 38
 
2.5%
ValueCountFrequency (%)
10 38
 
2.5%
9 86
 
5.7%
8 179
11.9%
7 211
14.1%
6 317
21.1%
5 254
16.9%
4 186
12.4%
3 114
 
7.6%
2 82
 
5.5%
1 33
 
2.2%

Work_Life_Balance
Real number (ℝ)

Distinct10
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2213333
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.8 KiB
2025-07-20T18:30:07.224030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q38
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.0882014
Coefficient of variation (CV)0.33565175
Kurtosis-0.28124157
Mean6.2213333
Median Absolute Deviation (MAD)1
Skewness-0.33881504
Sum9332
Variance4.3605853
MonotonicityNot monotonic
2025-07-20T18:30:07.270875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 303
20.2%
7 279
18.6%
8 217
14.5%
5 194
12.9%
4 132
8.8%
9 130
8.7%
3 91
 
6.1%
10 76
 
5.1%
2 46
 
3.1%
1 32
 
2.1%
ValueCountFrequency (%)
1 32
 
2.1%
2 46
 
3.1%
3 91
 
6.1%
4 132
8.8%
5 194
12.9%
6 303
20.2%
7 279
18.6%
8 217
14.5%
9 130
8.7%
10 76
 
5.1%
ValueCountFrequency (%)
10 76
 
5.1%
9 130
8.7%
8 217
14.5%
7 279
18.6%
6 303
20.2%
5 194
12.9%
4 132
8.8%
3 91
 
6.1%
2 46
 
3.1%
1 32
 
2.1%
Distinct122
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
Minimum2024-01-15 00:00:00
Maximum2024-05-15 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-07-20T18:30:07.511312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:07.587734image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Response_Quality
Categorical

Distinct3
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size11.8 KiB
High
1053 
Medium
367 
Low
 
80

Length

Max length6
Median length4
Mean length4.436
Min length3

Characters and Unicode

Total characters6654
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMedium
2nd rowHigh
3rd rowHigh
4th rowHigh
5th rowHigh

Common Values

ValueCountFrequency (%)
High 1053
70.2%
Medium 367
 
24.5%
Low 80
 
5.3%

Length

2025-07-20T18:30:07.654253image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-07-20T18:30:07.691534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
high 1053
70.2%
medium 367
 
24.5%
low 80
 
5.3%

Most occurring characters

ValueCountFrequency (%)
i 1420
21.3%
H 1053
15.8%
g 1053
15.8%
h 1053
15.8%
M 367
 
5.5%
e 367
 
5.5%
d 367
 
5.5%
u 367
 
5.5%
m 367
 
5.5%
L 80
 
1.2%
Other values (2) 160
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6654
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 1420
21.3%
H 1053
15.8%
g 1053
15.8%
h 1053
15.8%
M 367
 
5.5%
e 367
 
5.5%
d 367
 
5.5%
u 367
 
5.5%
m 367
 
5.5%
L 80
 
1.2%
Other values (2) 160
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6654
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 1420
21.3%
H 1053
15.8%
g 1053
15.8%
h 1053
15.8%
M 367
 
5.5%
e 367
 
5.5%
d 367
 
5.5%
u 367
 
5.5%
m 367
 
5.5%
L 80
 
1.2%
Other values (2) 160
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6654
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 1420
21.3%
H 1053
15.8%
g 1053
15.8%
h 1053
15.8%
M 367
 
5.5%
e 367
 
5.5%
d 367
 
5.5%
u 367
 
5.5%
m 367
 
5.5%
L 80
 
1.2%
Other values (2) 160
 
2.4%

Interactions

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2025-07-20T18:29:56.034377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:56.818993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:57.565847image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:58.504627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:59.232202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:00.005562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:00.751754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:01.661776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:02.374604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:03.147531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:52.080703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:53.141720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:54.141323image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:55.240208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:56.100714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:56.874346image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:57.622454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:58.557325image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:59.284474image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:00.063048image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:00.807229image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:01.715116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:02.426865image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:03.193002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:52.155450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:53.219905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:54.206863image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:55.300996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:56.151034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:56.927265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:57.673245image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:58.609438image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:29:59.337649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:00.115427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:00.858161image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:01.767452image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-07-20T18:30:02.473666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-07-20T18:30:07.748104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
AgeCommute_Time_MinutesCompany_SizeDepartmentEducation_LevelEfficiency_RatingGenderHas_ChildrenHome_Office_QualityIndustryInnovation_ScoreInternet_Speed_CategoryJob_LevelJob_SatisfactionLocation_TypeManager_Support_LevelMarital_StatusMeetings_Per_WeekProductivity_ScoreQuality_ScoreResponse_QualityStress_LevelTask_Completion_RateTeam_Collaboration_FrequencyWFH_Days_Per_WeekWork_Hours_Per_WeekWork_Life_BalanceYears_Experience
Age1.000-0.0110.0000.0000.0000.2540.0430.0090.0560.0480.1800.0810.3150.1340.0500.0250.0000.0480.2650.2540.000-0.0200.2380.0000.013-0.036-0.0300.697
Commute_Time_Minutes-0.0111.0000.0170.0000.046-0.0200.0000.0240.0000.000-0.0440.0000.000-0.0270.0000.0000.0000.021-0.016-0.0260.058-0.012-0.0380.000-0.4560.0250.0000.012
Company_Size0.0000.0171.0000.0190.0000.0000.0000.0000.0430.0000.0640.0000.0000.0000.0000.0000.0000.0470.0270.0280.0370.0390.0000.0300.0530.0220.0000.000
Department0.0000.0000.0191.0000.0000.0430.0360.0000.0550.0160.0430.0290.0110.0000.0000.0000.0180.0000.0370.0260.0410.0300.0520.0000.0120.0120.0290.000
Education_Level0.0000.0460.0000.0001.0000.0380.0700.0000.0320.0000.0080.0000.0160.0780.0510.0170.0280.0340.0000.0000.0530.0000.0390.0330.0160.0000.0240.045
Efficiency_Rating0.254-0.0200.0000.0430.0381.0000.0810.0000.0640.0000.6290.0000.0850.4340.0760.1320.000-0.0050.7990.7340.000-0.0090.7050.032-0.009-0.1030.0210.279
Gender0.0430.0000.0000.0360.0700.0811.0000.0000.0170.0000.0630.0190.0000.0000.0000.0250.0000.0600.0430.0820.0000.0130.0660.0450.0000.0180.0000.000
Has_Children0.0090.0240.0000.0000.0000.0000.0001.0000.0000.0260.0000.0310.0000.0650.0000.0000.0640.0480.0780.0000.0000.0350.0000.0000.0000.0210.0230.000
Home_Office_Quality0.0560.0000.0430.0550.0320.0640.0170.0001.0000.0000.0410.0000.0310.0560.0190.0250.0000.0480.0990.0790.0320.0000.0840.0000.0000.0000.0000.000
Industry0.0480.0000.0000.0160.0000.0000.0000.0260.0001.0000.0160.0000.0290.0110.0420.0000.0450.0000.0300.0000.0550.0300.0500.0000.0310.0000.0210.058
Innovation_Score0.180-0.0440.0640.0430.0080.6290.0630.0000.0410.0161.0000.0590.0970.4010.0000.1340.0000.0070.7350.6760.0320.0040.6350.062-0.003-0.0080.0150.274
Internet_Speed_Category0.0810.0000.0000.0290.0000.0000.0190.0310.0000.0000.0591.0000.0340.0000.0000.0410.0420.0180.0000.0310.0000.0000.0000.0220.0000.0000.0000.036
Job_Level0.3150.0000.0000.0110.0160.0850.0000.0000.0310.0290.0970.0341.0000.0000.0390.0180.0000.0000.1120.1050.0000.0000.1020.0330.0000.0000.0320.517
Job_Satisfaction0.134-0.0270.0000.0000.0780.4340.0000.0650.0560.0110.4010.0000.0001.0000.0090.2630.0000.0090.4600.4200.000-0.0070.4070.044-0.003-0.0450.0240.155
Location_Type0.0500.0000.0000.0000.0510.0760.0000.0000.0190.0420.0000.0000.0390.0091.0000.0000.0390.0220.0330.0000.0000.0000.0000.0000.0560.0000.0000.000
Manager_Support_Level0.0250.0000.0000.0000.0170.1320.0250.0000.0250.0000.1340.0410.0180.2630.0001.0000.0000.0000.1530.1430.0000.0210.1220.0220.0000.0110.0120.000
Marital_Status0.0000.0000.0000.0180.0280.0000.0000.0640.0000.0450.0000.0420.0000.0000.0390.0001.0000.0000.0150.0270.0000.0000.0000.0000.0000.0670.0000.000
Meetings_Per_Week0.0480.0210.0470.0000.034-0.0050.0600.0480.0480.0000.0070.0180.0000.0090.0220.0000.0001.0000.001-0.0010.019-0.0140.0210.000-0.0250.0160.0340.040
Productivity_Score0.265-0.0160.0270.0370.0000.7990.0430.0780.0990.0300.7350.0000.1120.4600.0330.1530.0150.0011.0000.8770.000-0.0140.8440.0000.010-0.0290.0370.311
Quality_Score0.254-0.0260.0280.0260.0000.7340.0820.0000.0790.0000.6760.0310.1050.4200.0000.1430.027-0.0010.8771.0000.0640.0090.7560.0000.015-0.0350.0270.302
Response_Quality0.0000.0580.0370.0410.0530.0000.0000.0000.0320.0550.0320.0000.0000.0000.0000.0000.0000.0190.0000.0641.0000.0430.0000.0000.0000.0000.0000.032
Stress_Level-0.020-0.0120.0390.0300.000-0.0090.0130.0350.0000.0300.0040.0000.000-0.0070.0000.0210.000-0.014-0.0140.0090.0431.000-0.0310.007-0.028-0.005-0.015-0.032
Task_Completion_Rate0.238-0.0380.0000.0520.0390.7050.0660.0000.0840.0500.6350.0000.1020.4070.0000.1220.0000.0210.8440.7560.000-0.0311.0000.0000.026-0.0230.0220.267
Team_Collaboration_Frequency0.0000.0000.0300.0000.0330.0320.0450.0000.0000.0000.0620.0220.0330.0440.0000.0220.0000.0000.0000.0000.0000.0070.0001.0000.0250.0000.0200.000
WFH_Days_Per_Week0.013-0.4560.0530.0120.016-0.0090.0000.0000.0000.031-0.0030.0000.000-0.0030.0560.0000.000-0.0250.0100.0150.000-0.0280.0260.0251.0000.005-0.0330.011
Work_Hours_Per_Week-0.0360.0250.0220.0120.000-0.1030.0180.0210.0000.000-0.0080.0000.000-0.0450.0000.0110.0670.016-0.029-0.0350.000-0.005-0.0230.0000.0051.0000.036-0.017
Work_Life_Balance-0.0300.0000.0000.0290.0240.0210.0000.0230.0000.0210.0150.0000.0320.0240.0000.0120.0000.0340.0370.0270.000-0.0150.0220.020-0.0330.0361.000-0.023
Years_Experience0.6970.0120.0000.0000.0450.2790.0000.0000.0000.0580.2740.0360.5170.1550.0000.0000.0000.0400.3110.3020.032-0.0320.2670.0000.011-0.017-0.0231.000

Missing values

2025-07-20T18:30:03.295031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-07-20T18:30:03.431312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Employee_IDAgeYears_ExperienceWFH_Days_Per_WeekGenderEducation_LevelMarital_StatusHas_ChildrenLocation_TypeDepartmentJob_LevelCompany_SizeIndustryHome_Office_QualityInternet_Speed_CategoryWork_Hours_Per_WeekManager_Support_LevelTeam_Collaboration_FrequencyProductivity_ScoreTask_Completion_RateQuality_ScoreInnovation_ScoreEfficiency_RatingMeetings_Per_WeekCommute_Time_MinutesJob_SatisfactionStress_LevelWork_Life_BalanceSurvey_DateResponse_Quality
0EMP000139102FemaleAssociate DegreeMarriedYesUrbanProductMid-LevelLarge (1001-5000)FinanceGoodVery Fast (100+ Mbps)41LowFew times per week52.256.658.152.172.144855.9682024-04-05Medium
1EMP00023345FemaleMaster DegreeMarriedNoUrbanCustomer SuccessSeniorStartup (1-50)EducationGoodVery Fast (100+ Mbps)52ModerateMonthly81.570.893.377.989.512096.1382024-01-29High
2EMP00034033MalePhDSingleYesRuralOperationsMid-LevelMedium (201-1000)TechnologyExcellentFast (50-100 Mbps)43ModerateFew times per week82.281.984.763.295.0152490.4562024-01-18High
3EMP000448143MaleBachelor DegreeMarriedYesUrbanFinanceManagerMedium (201-1000)TechnologyExcellentVery Fast (100+ Mbps)45HighDaily75.670.267.882.595.088100.01052024-04-18High
4EMP00053265MaleHigh SchoolDivorcedYesRuralEngineeringSeniorSmall (51-200)TechnologyAverageModerate (25-50 Mbps)42Very LowFew times per week98.098.286.467.595.0100100.0342024-02-19High
5EMP00063274MaleBachelor DegreeIn RelationshipNoUrbanHRMid-LevelMedium (201-1000)TechnologyGoodVery Fast (100+ Mbps)43Very HighFew times per week62.059.860.644.272.5749100.0752024-02-15Medium
6EMP00074912FemaleHigh SchoolIn RelationshipNoSuburbanMarketingJuniorEnterprise (5000+)EducationAverageModerate (25-50 Mbps)38ModerateFew times per week66.275.065.879.490.01012100.0832024-02-12Medium
7EMP000841110FemaleBachelor DegreeMarriedNoSuburbanSalesManagerLarge (1001-5000)MediaExcellentVery Fast (100+ Mbps)45ModerateWeekly77.275.086.375.294.8713100.0452024-02-01High
8EMP00093044Non-binaryMaster DegreeSingleNoUrbanEngineeringSeniorStartup (1-50)TechnologyAverageVery Fast (100+ Mbps)47ModerateWeekly73.869.089.176.193.032592.4872024-04-18Medium
9EMP00103994FemalePhDDivorcedYesUrbanEngineeringSeniorLarge (1001-5000)MediaGoodSlow (<25 Mbps)32HighDaily72.477.083.680.287.4631100.0442024-01-28High
Employee_IDAgeYears_ExperienceWFH_Days_Per_WeekGenderEducation_LevelMarital_StatusHas_ChildrenLocation_TypeDepartmentJob_LevelCompany_SizeIndustryHome_Office_QualityInternet_Speed_CategoryWork_Hours_Per_WeekManager_Support_LevelTeam_Collaboration_FrequencyProductivity_ScoreTask_Completion_RateQuality_ScoreInnovation_ScoreEfficiency_RatingMeetings_Per_WeekCommute_Time_MinutesJob_SatisfactionStress_LevelWork_Life_BalanceSurvey_DateResponse_Quality
1490EMP14913014MalePhDMarriedYesUrbanHRJuniorStartup (1-50)TechnologyGoodVery Fast (100+ Mbps)44ModerateFew times per week72.072.086.759.993.5113086.5572024-05-15High
1491EMP149258121MaleBachelor DegreeMarriedYesSuburbanOperationsSeniorSmall (51-200)FinanceGoodFast (50-100 Mbps)35ModerateMonthly94.5100.090.294.995.060100.0662024-01-30High
1492EMP14932944FemaleBachelor DegreeMarriedYesSuburbanProductMid-LevelSmall (51-200)TechnologyAverageFast (50-100 Mbps)40ModerateDaily98.098.196.393.695.0520100.0652024-04-06Low
1493EMP14943680FemaleBachelor DegreeDivorcedNoSuburbanEngineeringMid-LevelEnterprise (5000+)HealthcareGoodFast (50-100 Mbps)40ModerateDaily95.098.594.882.895.0931100.0672024-04-30High
1494EMP14954820FemaleBachelor DegreeMarriedNoSuburbanEngineeringMid-LevelMedium (201-1000)Non-profitPoorModerate (25-50 Mbps)45ModerateDaily52.040.050.034.464.723477.6662024-02-03High
1495EMP14965355MaleMaster DegreeMarriedNoUrbanHRSeniorStartup (1-50)EducationGoodFast (50-100 Mbps)36ModerateWeekly78.589.784.158.389.49071.4852024-03-18High
1496EMP14975393Non-binaryMaster DegreeMarriedYesSuburbanFinanceMid-LevelSmall (51-200)TechnologyAverageVery Fast (100+ Mbps)43ModerateWeekly74.771.974.754.287.231195.8882024-05-13High
1497EMP14984524MaleAssociate DegreeSingleYesSuburbanEngineeringJuniorLarge (1001-5000)TechnologyAverageModerate (25-50 Mbps)51Very HighWeekly88.692.988.471.295.01134100.0452024-04-15High
1498EMP149944124FemaleBachelor DegreeSingleYesSuburbanOperationsSeniorLarge (1001-5000)TechnologyAverageFast (50-100 Mbps)26Very HighDaily92.174.293.988.695.0106100.0362024-02-21Medium
1499EMP15004045MaleBachelor DegreeDivorcedYesSuburbanCustomer SuccessMid-LevelEnterprise (5000+)TechnologyPoorVery Fast (100+ Mbps)40ModerateBi-weekly71.464.164.864.981.0120100.0572024-03-20Low